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      Catastrophic health expenditure of households with hypertension: a comparative study in China

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          Abstract

          Objectives

          The aim of this study was to understand the impact of multimorbidity on catastrophic health expenditures for people with hypertension.

          Methods

          Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS) in 2018, 8,342 adults were included in our analysis. Propensity score matching method was used to compare the risk of catastrophic health expenditures between the hypertension patients (treatment group) and those without any chronic disease (control group) in middle-aged and older adults. Patients with hypertension were also divided into two groups: only hypertension and multimorbidity.

          Results

          Hypertension increased the likelihood of CHE by 11.3% in older adults. Further analysis showed that hypertension alone does not increase the risk of CHE, and the risk of CHE in hypertension patients with multimorbidity was 12.9% higher than those without chronic disease.

          Conclusion

          Our study highlights the importance of healthy management of patients with only hypertension and preventing them from developing multimorbidity.

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          Most cited references47

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          Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

          Summary Background Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding Bill & Melinda Gates Foundation.
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            The central role of the propensity score in observational studies for causal effects

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              Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS).

              The China Health and Retirement Longitudinal Study (CHARLS) is a nationally representative longitudinal survey of persons in China 45 years of age or older and their spouses, including assessments of social, economic, and health circumstances of community-residents. CHARLS examines health and economic adjustments to rapid ageing of the population in China. The national baseline survey for the study was conducted between June 2011 and March 2012 and involved 17 708 respondents. CHARLS respondents are followed every 2 years, using a face-to-face computer-assisted personal interview (CAPI). Physical measurements are made at every 2-year follow-up, and blood sample collection is done once in every two follow-up periods. A pilot survey for CHARLS was conducted in two provinces of China in 2008, on 2685 individuals, who were resurveyed in 2012. To ensure the adoption of best practices and international comparability of results, CHARLS was harmonized with leading international research studies in the Health and Retirement Study (HRS) model. Requests for collaborations should be directed to Dr Yaohui Zhao (yhzhao@nsd.edu.cn). All data in CHARLS are maintained at the National School of Development of Peking University and will be accessible to researchers around the world at the study website. The 2008 pilot data for CHARLS are available at: http://charls.ccer.edu.cn/charls/. National baseline data for the study are expected to be released in January 2013.

                Author and article information

                Contributors
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                08 June 2023
                2023
                : 11
                : 1176170
                Affiliations
                [1] 1School of Public Health, Health Science Center, Xi’an Jiaotong University , Xi’an, Shaanxi, China
                [2] 2School of Public Policy and Administration, Xi’an Jiaotong University , Xi’an, Shaanxi, China
                Author notes

                Edited by: Ozden Gokdemir, İzmir University of Economics, Türkiye

                Reviewed by: Huseyin Elbi, Manisa Celal Bayar University School of Medicine, Türkiye; Yibo Wu, Peking University, China

                *Correspondence: Zhongliang Zhou, zzliang1981@ 123456163.com
                Article
                10.3389/fpubh.2023.1176170
                10285052
                37361148
                ebd7e460-37d0-4cc7-b5a9-86e059502a5f
                Copyright © 2023 Zhai, Zhou, Liu, Lu, Zhao, Cao, Zhao, Lai and Fan.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 February 2023
                : 18 May 2023
                Page count
                Figures: 2, Tables: 4, Equations: 5, References: 47, Pages: 10, Words: 7101
                Funding
                Funded by: Shaanxi Social Science Foundation
                Award ID: 2017S024
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 71874137
                Categories
                Public Health
                Original Research
                Custom metadata
                Health Economics

                hypertension,catastrophic health expenditure,multimorbidity,propensity score matching,china

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